Natural Language Processing

The Coursera Data Science Capstone Project requires us to develop Natural Language Process and Prediction Models and to present our work though a Shiny App as well as provide some details in this Slidify Presentation. SwiftKey provided data which can be found on CloudFront.Net

SwiftKey provided us with text data from Blogs, News, and Twitter which allowed us to build our N-Gram Model. As you can imagine, the data was not in a very usable state. So we cleaned the data, and removed inappropriate words. From this we were able to build our N-Gram and Prediction Models. We are able to throttle the size of the models to allow for limited resources, but this causes predictions to be less accurate. Our larger size models, allow for better predictions. The size of the model doesn't affect performance speed, and this is key in providing fast reaction time for our users.

For a detailed description, please review the article found at WikiPedia.org.

You can find a demo of our larger prediction model here. The default prediction model is based on our analysis of the data provide by SwiftKey. However, as a bonus, we have included in a separate tab an N-Gram model which uses the works of Edgar Alan Poe. Needless to say, the results are interesting and illustrate the flexibility and accuracy of our models.